Towards interactive Machine Learning (iML): Applying Ant Colony Algorithms to Solve the Traveling Salesman Problem with the Human-in-the-Loop Approach

By Andreas Holzinger, Markus Plass, Katharina Holzinger, Gloria Cerasela Crisan, Camelia-M. Pintea, Vasile Palade. 2016

97 citations recorded by the source corpus.

Most Machine Learning (ML) researchers focus on automatic Machine Learning (aML) where great advances have been made, for example, in speech recognition, recommender systems, or autonomous vehicles. Automatic approaches greatly benefit from the availability of “big data”. However, sometimes, for example in health informatics, we are confronted not a small number of data sets or rare events, and with complex problems where aML-approaches fail or deliver unsatisfactory results. Here, interactive Machine Learning (iML) may be of help and the “human-in-the-loop” approach may be beneficial in solving computationally hard problems, where human expertise can help to reduce an exponential search space through heuristics.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

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Citations in the corpus

What do we need to build explainable AI systems for the medical domain? (2017, background): “Very useful for building explainable systems in the medical domain is generally ge-netic programming (Koza, 1994; Pena-Reyes and Sipper, 1999; Tsakonas et al., 2004), and speciﬁcally evolutionary algorithms (Wang and Palade, 2011; Holzinger et al., 2014, 2016).”
A Review of User Interface Design for Interactive Machine Learning (2018, methodology): “Holzinger et al. (2016) incorporate human feedback with an Ant Colony Optimisation (ACO) framework to solve a traveling salesman problem.”
A glass-box interactive machine learning approach for solving NP-hard problems with the human-in-the-loop (2017, methodology): “…human-in-the-loop (e.g., a human kernel (Wilson et al., 2015)), or the involvement of a human directly into the algorithm, iML is deﬁned by ”algorithms which interact with agents and can optimize their learning behaviour through this interaction – where the agents can be humans (Holzinger, 2016a)”.”
Introduction to MAchine Learning & Knowledge Extraction (MAKE) (2017, background): “A recent experimental work [40] demonstrates the usefulness on the Traveling Salesman Problem (TSP), which appears in a number of practical problems, e.”